Recently, several latent topic analysis methods such as LSI, pLSI, and LDA have been widely used for text analysis. However, those methods basically assign topics to words, but do not account for the events in a document. With this background, in this paper, we propose a latent topic extracting method which assigns topics to events. We also show that our proposed method is useful to generate a document summary based on a latent topic.

A Latent Topic Extracting Method based on Events in a Document and its Application

This paper proposed a new method of web news summarization via soft clustering algorithm. It used search engine to extract relevant documents, and mixed query sentence into sentences set which segmented from multi-document set, then this paper adopted efficient soft cluster algorithm SSSC to cluster all the sentences. If the number of cluster which contains the query sentence is larger than or equal to 5, the summary sentence will be extracted by turns from the clusters which query sentence in, or feature fusion will be used to extract summary sentences. Experimental result shows that the proposed summarization method can improve the performance of summary, soft clustering algorithm is efficient.

An improved web information summarization based on SSSC

This paper describes a way of performing alignment of Portuguese-Chinese bilingual pairs from the given two documents. Extracting the alignment pairs is a critical step for building Portuguese-Chinese bilingual corpus for Example Based Machine Translation Systems (EBMT). In short, the proposed alignment system performs four steps: break down the document into sentences level, score each pair of sentence by different features, apply Singular Value Decomposition on the results, extract the aligned pair base on a similarity function.

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Cross sentence alignment based on singular value decomposition

A multi-document summarization method based on Latent Semantic Indexing (LSI) is proposed. The method combines several reports on the same issue into a matrix of terms and sentences, and uses a Singular Value Decomposition (SVD) to reduce the dimension of the matrix and extract features, and then the sentence similarity is computed. The sentences are clustered according to similarity of sentences. The centroid sentences are selected from each class. Finally, the selected sentences are ordered to generate the summarization. The evaluation and results are presented, which prove that the proposed methods are efficient.

李生

Papers

Research on multi-document summarization based on latent semantic indexing